Using Stable Diffision models for Colorization

[Dan64]

Hi didris,

   I just released this project: DiTServerRPC
   It is a XML-RPC server that exposes a GPU-accelerated colorization pipeline for black-and-white images and video frames. 
   It is built on top of Nunchaku SVDQuant FP4/INT4 transformer and the `Qwen-Image-Edit-2511` diffusion model.

  In the project there are the instructions to install the server.
  The server can use both FP4 (RTX 50-Series, Blackwell) and INT4 (RTX 30/40-Series Ampere/Ada Lovelace) quantization.

   You can execute the server with the command (select fp4 or int4) 

.venv\Scripts\activate
# RTX 50-Series
python dit_rpc_server.py --load-pipeline --pipeline-config qwen_config_fp4.json

# RTX 30 / 40-Series
python dit_rpc_server.py --load-pipeline --pipeline-config qwen_config_int4.json

   Once the server is running, open another terminal in the project directory and run

.venv\Scripts\activate
# RTX 50-Series
python dit_client_pair_example.py --pipeline-config qwen_config_fp4.json --use-shm

# RTX 30 / 40-Series
python dit_client_pair_example.py --pipeline-config qwen_config_int4.json --use-shm

  Depending on your GPU you should obtain an inference speed between 4-5 sec. per image.

Dan

P.S.
If you like this project please star it.